國立虎尾科技大學 |

Toward Leveraging Artificial Intelligence to Support the Identification of Accessibility Challenges.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Toward Leveraging Artificial Intelligence to Support the Identification of Accessibility Challenges./
Author:	Aljedaani, Wajdi Mohammed R., Sr.
Description:	1 online resource (227 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-02, Section: A.
Contained By:	Dissertations Abstracts International85-02A.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9798380070065

Toward Leveraging Artificial Intelligence to Support the Identification of Accessibility Challenges.
Aljedaani, Wajdi Mohammed R., Sr.

Toward Leveraging Artificial Intelligence to Support the Identification of Accessibility Challenges. - 1 online resource (227 pages)

Source: Dissertations Abstracts International, Volume: 85-02, Section: A.

Thesis (Ph.D.)--University of North Texas, 2023.

Includes bibliographical references

The goal of this thesis is to support the automated identification of accessibility in user reviews or bug reports, to help technology professionals prioritize their handling, and, thus, to create more inclusive apps. Particularly, we propose a model that takes as input accessibility user reviews or bug reports and learns their keyword-based features to make a classification decision, for a given review, on whether it is about accessibility or not. Our empirically driven study follows a mixture of qualitative and quantitative methods. We introduced models that can accurately identify accessibility reviews and bug reports and automate detecting them. Our models can automatically classify app reviews and bug reports as accessibility-related or not so developers can easily detect accessibility issues with their products and improve them to more accessible and inclusive apps utilizing the users' input. Our goal is to create a sustainable change by including a model in the developer's software maintenance pipeline and raising awareness of existing errors that hinder the accessibility of mobile apps, which is a pressing need. In light of our findings from the Blackboard case study, Blackboard and the course material are not easily accessible to deaf students and hard of hearing. Thus, deaf students find that learning is extremely stressful during the pandemic.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380070065Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

AccessibilityIndex Terms--Genre/Form:

554714
Electronic books.

Toward Leveraging Artificial Intelligence to Support the Identification of Accessibility Challenges.
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500 $a Advisor: Ludi, Stephanie.
502 $a Thesis (Ph.D.)--University of North Texas, 2023.
504 $a Includes bibliographical references
520 $a The goal of this thesis is to support the automated identification of accessibility in user reviews or bug reports, to help technology professionals prioritize their handling, and, thus, to create more inclusive apps. Particularly, we propose a model that takes as input accessibility user reviews or bug reports and learns their keyword-based features to make a classification decision, for a given review, on whether it is about accessibility or not. Our empirically driven study follows a mixture of qualitative and quantitative methods. We introduced models that can accurately identify accessibility reviews and bug reports and automate detecting them. Our models can automatically classify app reviews and bug reports as accessibility-related or not so developers can easily detect accessibility issues with their products and improve them to more accessible and inclusive apps utilizing the users' input. Our goal is to create a sustainable change by including a model in the developer's software maintenance pipeline and raising awareness of existing errors that hinder the accessibility of mobile apps, which is a pressing need. In light of our findings from the Blackboard case study, Blackboard and the course material are not easily accessible to deaf students and hard of hearing. Thus, deaf students find that learning is extremely stressful during the pandemic.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Audiology. $3 889171
650 4 $a Disability studies. $3 809395
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653 $a User reviews
653 $a Machine learning
653 $a Deaf students
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